Abstract TP306: Characteristics and Outcomes Among Patients Transferred to Regional Stroke Centers Across the United States for Specialized Stroke Care
Bibliographic record
Abstract
Intro: Many patients are transferred to stroke centers for advanced stroke care, especially after IV tPA. We sought to determine differences in the baseline characteristics and outcomes between AIS cases presenting directly to stroke centers’ front doors vs. transfers-in from another regional acute care hospital. Methods: Using data from the national GWTG-Stroke registry, we analyzed 970,390 AIS cases (01/2010 - 03/14). Patients at hospitals with high transfer-in rates (>15%) were selected (284 hospitals, 303,739 patients). Due to large sample size, instead of p-values, standardized differences were reported. Multivariable model (MV) examined the association of transfer-in vs. front door with the primary and secondary outcomes, adjusting for patient and hospital characteristics including NIHSS. Results: High volume transfer-in hospitals admitted 31% of their patients via transfer. Transfer-in patients were younger, more often white and non-Hispanic. They had similar stroke risk factors except for hypertension and previous stroke/TIA which were less common. Transfer-in had worse initial NIHSS, more often had altered consciousness and language disturbance. Transfer-in patients had longer length of hospital stay, higher mRS at discharge, and were less often discharged home. In-hospital mortality was ∼ 3% higher in transfer-in as compared with front-door. Among tPA treated patients, sICH < 36hr was more common in transfer-in patients. On MV, transfer-in patients had overall worse outcomes as shown by the higher odds of in-hospital mortality, longer length of stay, and not able to ambulate independently at discharge (Table). Conclusion: Many hospitals receive high volumes of stroke patients via transfer. Because transfer-in patients have worse outcomes, these patients have the potential to negatively influence institutional outcomes rates. Transfer-in patients should be carefully accounted for in risk adjusted models of hospital outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".